ArticleScience (New York, N.Y.)2010
NMR structure determination for larger proteins using backbone-only data.
Article in Science (New York, N.Y.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers.
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Who cites it
150 citing papers in PubMed.
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- New insights into GATOR2-dependent interactions and its conformational changes in amino acid sensing.Bioscience reports · 2024Article
- Recent Advances in NMR Protein Structure Prediction with ROSETTA.International journal of molecular sciences · 2023Review
- Agl24 is an ancient archaeal homolog of the eukaryotic N-glycan chitobiose synthesis enzymes.eLife · 2022Article
- Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes.Communications biology · 2022Article
- Structural Analysis of the Effect of Asn107Ser Mutation on Alg13 Activity and Alg13-Alg14 Complex Formation and Expanding the Phenotypic Variability of ALG13-CDG.Biomolecules · 2022Article
- Concurrent Identification and Characterization of Protein Structure and Continuous Internal Dynamics with REDCRAFT.Frontiers in molecular biosciences · 2022Article
- Bipartite binding and partial inhibition links DEPTOR and mTOR in a mutually antagonistic embrace.eLife · 2021Article
- Structure of membrane diacylglycerol kinase in lipid bilayers.Communications biology · 2021Article
- An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling.Frontiers in molecular biosciences · 2021Article
- Chemical shifts-based similarity restraints improve accuracy of RNA structures determined via NMR.RNA (New York, N.Y.) · 2020Article
- Macromolecular modeling and design in Rosetta: recent methods and frameworks.Nature methods · 2020Review
- Determining methyl sidechain conformations in a CS-ROSETTA model using methylJournal of biomolecular NMR · 2020Article
- Paramagpy: software for fitting magnetic susceptibility tensors using paramagnetic effects measured in NMR spectra.Magnetic resonance (Gottingen, Germany) · 2020Article
- Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13.Proteins · 2019Article
- Article
- Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints.Structure (London, England : 1993) · 2019Article
- Unraveling the structure and dynamics of the human DNAJB6b chaperone by NMR reveals insights into Hsp40-mediated proteostasis.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Emerging solution NMR methods to illuminate the structural and dynamic properties of proteins.Current opinion in structural biology · 2019Review
90 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Conventional protein structure determination from nuclear magnetic resonance data relies heavily on side-chain proton-to-proton distances. The necessary side-chain resonance assignment, however, is labor intensive and prone to error. Here we show that structures can be accurately determined without nuclear magnetic resonance (NMR) information on the side chains for proteins up to 25 kilodaltons by incorporating backbone chemical shifts, residual dipolar couplings, and amide proton distances into the Rosetta protein structure modeling methodology. These data, which are too sparse for conventional methods, serve only to guide conformational search toward the lowest-energy conformations in the folding landscape; the details of the computed models are determined by the physical chemistry implicit in the Rosetta all-atom energy function. The new method is not hindered by the deuteration required to suppress nuclear relaxation processes for proteins greater than 15 kilodaltons and should enable routine NMR structure determination for larger proteins.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.